CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis by Ruixue Wang, Ning Zhao

“…To address this problem, this paper proposes a feature extraction method based on the combination of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Fuzzy Entropy (FN). …”
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Prediction of the monthly river water level by using ensemble decomposition modeling by Chaitanya Baliram Pande, Lariyah Mohd Sidek, Bijay Halder, Okan Mert Katipoğlu, Jitendra Rajput, Fahad Alshehri, Rabin Chakrabortty, Subodh Chandra Pal, Norlida Mohd Dom, Miklas Scholz

“…In this paper, developed the hybrid modeling combined with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), along with standalone models support vector machine (SVM-linear), and Random Forest (RF), Random Subspace (RS) for accurate prediction of monthly river water level in Sg Muar at Buloh Kasap, Johor station during 2014 to 2023. …”
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Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…We integrate complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose carbon emission time series into intrinsic mode functions (IMFs) capturing different frequency bands. …”
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Multi-Level Decomposition and Interpretability-Enhanced Air Conditioning Load Forecasting Study by Xinting Yang, Ling Zhang, Hong Zhao, Wenhua Zhang, Chuan Long, Gang Wu, Junhao Zhao, Xiaodong Shen

“…Given the limitations of traditional forecasting models in capturing different frequency components and noise within complex load sequences, this paper proposes a multi-level decomposition forecasting model using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), variational mode decomposition (VMD), and long short-term memory (LSTM). …”
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A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine by S. Syama, J. Ramprabhakar, R Anand, V. P. Meena, Josep M. Guerrero

“…The proposed integrated model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose time series data into a sequence of intrinsic mode functions of lower complexity. …”
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A Short-Term Electricity Load Complementary Forecasting Method Based on Bi-Level Decomposition and Complexity Analysis by Xun Dou, Yu He

“…Firstly, a Hodrick Prescott Filter (HP Filter) is used to decompose the electricity data, extracting the trend and periodic components. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is used to further decompose the periodic components to obtain several IMF components. …”
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Application of an improved LSTM model based on FECA and CEEMDAN VMD decomposition in water quality prediction by Jie Long, Chong Lu, Yiming Lei, Zhong Yuan Chen, Yihan Wang

“…Abstract To address the limitations of existing water quality prediction models in handling non-stationary data and capturing multi-scale features, this study proposes a hybrid model integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), Long Short-Term Memory Network (LSTM), and Frequency-Enhanced Channel Attention (FECA). …”
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Photovoltaic Power Generation Forecasting Based on Secondary Data Decomposition and Hybrid Deep Learning Model by Liwei Zhang, Lisang Liu, Wenwei Chen, Zhihui Lin, Dongwei He, Jian Chen

“…This paper proposes a learning model named CECSVB-LSTM, which integrates several advanced techniques: a bidirectional long short-term memory (BILSTM) network, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), variational mode decomposition (VMD), and the Sparrow Search Algorithm (CSSSA) incorporating circle chaos mapping and the Sine Cosine Algorithm. …”
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Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening. by Yaoyang Yi

“…First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the carbon price time series once, extract high-frequency and low-frequency components, and denoise the high-frequency components using stacked denoising autoencoder (SDAE). …”
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Collaborative Forecasting of Multiple Energy Loads in Integrated Energy Systems Based on Feature Extraction and Deep Learning by Zhe Wang, Jiali Duan, Fengzhang Luo, Xiaoyu Qiu

“…First, the complete ensemble empirical mode decomposition with adaptive noise algorithm decomposes load data, and a dynamic time warping-based k-medoids clustering algorithm reconstructs subsequences aligned with system load components. …”
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A Hybrid LMD–ARIMA–Machine Learning Framework for Enhanced Forecasting of Financial Time Series: Evidence from the NASDAQ Composite Index by Jawaria Nasir, Hasnain Iftikhar, Muhammad Aamir, Hasnain Iftikhar, Paulo Canas Rodrigues, Mohd Ziaur Rehman

“…This study presents a scalable and adaptive approach for modeling complex, nonlinear, and high-dimensional time series, thereby contributing to the enhancement of intelligent forecasting systems in the economic and financial sectors. …”
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Application of a Hybrid Model Based on CEEMDAN and IMSA in Water Quality Prediction by GUO Li-jin, WU Hao-tian

“…This study proposes a novel hybrid model for water quality prediction. [Methods] First, the original water quality sequence was decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). …”
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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods by Zhehao Huang, Benhuan Nie, Yuqiao Lan, Changhong Zhang

“…First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is employed to decompose carbon price data into distinct modal components, each defined by specific frequency characteristics. …”
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Enhanced Workload Prediction in Data Centers Using Two-Stage Decomposition and Hybrid Parallel Deep Learning by Dalal Alqahtani, Hamidreza Imani, Tarek El-Ghazawi

“…To improve this, we introduce CVCBM which blends signal processing techniques Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) with advanced deep learning models like Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. …”
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Short-term prediction of trimaran load based on data driven technology by Haoyun Tang, Rui Zhu, Qian Wan, Deyuan Ren

“…To highlight the trimaran high-frequency load fluctuation and improve the prediction accuracy, the LSTM neural network combines with different signal decomposition algorithms, such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Variational Mode Decomposition (VMD). …”
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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

“…This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms. By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and fuzzy entropy (FE) with the new and highly efficient Runge–Kuta optimizer (RUN), adaptive parameter optimization for the support vector machine (SVM) and radial basis function neural network (RBFNN) algorithms was achieved. …”
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A Combined Prediction Model for Hog Futures Prices Based on WOA-LightGBM-CEEMDAN by Xiang Wang, Shen Gao, Yibin Guo, Shiyu Zhou, Yonghui Duan, Daqing Wu

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GNSS Precipitable Water Vapor Prediction for Hong Kong Based on ICEEMDAN-SE-LSTM-ARIMA Hybrid Model by Jie Zhao, Xu Lin, Zhengdao Yuan, Nage Du, Xiaolong Cai, Cong Yang, Jun Zhao, Yashi Xu, Lunwei Zhao

“…In the study of GNSS-PWV prediction, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm within a decomposition–integration framework effectively addresses the non-stationarity and complexity of PWV sequences, enhancing prediction accuracy. …”
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Redefining volatility forecasting in the aerospace and defense sector: application of CEEMDAN-GARCH models by Viviane Naimy, Tatiana Abou Chedid, Omar Abou Saleh, Nicolas Bitar

“…Abstract This study pioneers the integration of Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and advanced GARCH models (IGARCH, SGARCH, and GJR-GARCH) to analyze the volatility of aerospace and defense indices across four countries: China, South Korea, France, and the United Kingdom. …”
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The role of hybrid models in financial decision-making: Forecasting stock prices with advanced algorithms by Xiaoyi Zhu

“…Additionally, it incorporates ensemble empirical mode decomposition, sample entropy clustering, and sea-horse optimizer as part of its methodology. …”
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